docker/docker-airflow/spark_files/spark-2.4.5-bin-hadoop2.6/examples/src/main/java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java
<reponame>LucasRangelSSouza/DatalakeDocker<filename>docker/docker-airflow/spark_files/spark-2.4.5-bin-hadoop2.6/examples/src/main/java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *    http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
package org.apache.spark.examples.sql;

// $example on:schema_merging$
import java.io.Serializable;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
// $example off:schema_merging$
import java.util.Properties;

// $example on:basic_parquet_example$
import org.apache.spark.api.java.function.MapFunction;
import org.apache.spark.sql.Encoders;
// $example on:schema_merging$
// $example on:json_dataset$
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
// $example off:json_dataset$
// $example off:schema_merging$
// $example off:basic_parquet_example$
import org.apache.spark.sql.SparkSession;

public class JavaSQLDataSourceExample {

  // $example on:schema_merging$
  public static class Square implements Serializable {
    private int value;
    private int square;

    // Getters and setters...
    // $example off:schema_merging$
    public int getValue() {
      return value;
    }

    public void setValue(int value) {
      this.value = value;
    }

    public int getSquare() {
      return square;
    }

    public void setSquare(int square) {
      this.square = square;
    }
    // $example on:schema_merging$
  }
  // $example off:schema_merging$

  // $example on:schema_merging$
  public static class Cube implements Serializable {
    private int value;
    private int cube;

    // Getters and setters...
    // $example off:schema_merging$
    public int getValue() {
      return value;
    }

    public void setValue(int value) {
      this.value = value;
    }

    public int getCube() {
      return cube;
    }

    public void setCube(int cube) {
      this.cube = cube;
    }
    // $example on:schema_merging$
  }
  // $example off:schema_merging$

  public static void main(String[] args) {
    SparkSession spark = SparkSession
      .builder()
      .appName("Java Spark SQL data sources example")
      .config("spark.some.config.option", "some-value")
      .getOrCreate();

    runBasicDataSourceExample(spark);
    runBasicParquetExample(spark);
    runParquetSchemaMergingExample(spark);
    runJsonDatasetExample(spark);
    runJdbcDatasetExample(spark);

    spark.stop();
  }

  private static void runBasicDataSourceExample(SparkSession spark) {
    // $example on:generic_load_save_functions$
    Dataset<Row> usersDF = spark.read().load("examples/src/main/resources/users.parquet");
    usersDF.select("name", "favorite_color").write().save("namesAndFavColors.parquet");
    // $example off:generic_load_save_functions$
    // $example on:manual_load_options$
    Dataset<Row> peopleDF =
      spark.read().format("json").load("examples/src/main/resources/people.json");
    peopleDF.select("name", "age").write().format("parquet").save("namesAndAges.parquet");
    // $example off:manual_load_options$
    // $example on:manual_load_options_csv$
    Dataset<Row> peopleDFCsv = spark.read().format("csv")
      .option("sep", ";")
      .option("inferSchema", "true")
      .option("header", "true")
      .load("examples/src/main/resources/people.csv");
    // $example off:manual_load_options_csv$
    // $example on:manual_save_options_orc$
    usersDF.write().format("orc")
      .option("orc.bloom.filter.columns", "favorite_color")
      .option("orc.dictionary.key.threshold", "1.0")
      .save("users_with_options.orc");
    // $example off:manual_save_options_orc$
    // $example on:direct_sql$
    Dataset<Row> sqlDF =
      spark.sql("SELECT * FROM parquet.`examples/src/main/resources/users.parquet`");
    // $example off:direct_sql$
    // $example on:write_sorting_and_bucketing$
    peopleDF.write().bucketBy(42, "name").sortBy("age").saveAsTable("people_bucketed");
    // $example off:write_sorting_and_bucketing$
    // $example on:write_partitioning$
    usersDF
      .write()
      .partitionBy("favorite_color")
      .format("parquet")
      .save("namesPartByColor.parquet");
    // $example off:write_partitioning$
    // $example on:write_partition_and_bucket$
    peopleDF
      .write()
      .partitionBy("favorite_color")
      .bucketBy(42, "name")
      .saveAsTable("people_partitioned_bucketed");
    // $example off:write_partition_and_bucket$

    spark.sql("DROP TABLE IF EXISTS people_bucketed");
    spark.sql("DROP TABLE IF EXISTS people_partitioned_bucketed");
  }

  private static void runBasicParquetExample(SparkSession spark) {
    // $example on:basic_parquet_example$
    Dataset<Row> peopleDF = spark.read().json("examples/src/main/resources/people.json");

    // DataFrames can be saved as Parquet files, maintaining the schema information
    peopleDF.write().parquet("people.parquet");

    // Read in the Parquet file created above.
    // Parquet files are self-describing so the schema is preserved
    // The result of loading a parquet file is also a DataFrame
    Dataset<Row> parquetFileDF = spark.read().parquet("people.parquet");

    // Parquet files can also be used to create a temporary view and then used in SQL statements
    parquetFileDF.createOrReplaceTempView("parquetFile");
    Dataset<Row> namesDF = spark.sql("SELECT name FROM parquetFile WHERE age BETWEEN 13 AND 19");
    Dataset<String> namesDS = namesDF.map(
        (MapFunction<Row, String>) row -> "Name: " + row.getString(0),
        Encoders.STRING());
    namesDS.show();
    // +------------+
    // |       value|
    // +------------+
    // |Name: Justin|
    // +------------+
    // $example off:basic_parquet_example$
  }

  private static void runParquetSchemaMergingExample(SparkSession spark) {
    // $example on:schema_merging$
    List<Square> squares = new ArrayList<>();
    for (int value = 1; value <= 5; value++) {
      Square square = new Square();
      square.setValue(value);
      square.setSquare(value * value);
      squares.add(square);
    }

    // Create a simple DataFrame, store into a partition directory
    Dataset<Row> squaresDF = spark.createDataFrame(squares, Square.class);
    squaresDF.write().parquet("data/test_table/key=1");

    List<Cube> cubes = new ArrayList<>();
    for (int value = 6; value <= 10; value++) {
      Cube cube = new Cube();
      cube.setValue(value);
      cube.setCube(value * value * value);
      cubes.add(cube);
    }

    // Create another DataFrame in a new partition directory,
    // adding a new column and dropping an existing column
    Dataset<Row> cubesDF = spark.createDataFrame(cubes, Cube.class);
    cubesDF.write().parquet("data/test_table/key=2");

    // Read the partitioned table
    Dataset<Row> mergedDF = spark.read().option("mergeSchema", true).parquet("data/test_table");
    mergedDF.printSchema();

    // The final schema consists of all 3 columns in the Parquet files together
    // with the partitioning column appeared in the partition directory paths
    // root
    //  |-- value: int (nullable = true)
    //  |-- square: int (nullable = true)
    //  |-- cube: int (nullable = true)
    //  |-- key: int (nullable = true)
    // $example off:schema_merging$
  }

  private static void runJsonDatasetExample(SparkSession spark) {
    // $example on:json_dataset$
    // A JSON dataset is pointed to by path.
    // The path can be either a single text file or a directory storing text files
    Dataset<Row> people = spark.read().json("examples/src/main/resources/people.json");

    // The inferred schema can be visualized using the printSchema() method
    people.printSchema();
    // root
    //  |-- age: long (nullable = true)
    //  |-- name: string (nullable = true)

    // Creates a temporary view using the DataFrame
    people.createOrReplaceTempView("people");

    // SQL statements can be run by using the sql methods provided by spark
    Dataset<Row> namesDF = spark.sql("SELECT name FROM people WHERE age BETWEEN 13 AND 19");
    namesDF.show();
    // +------+
    // |  name|
    // +------+
    // |Justin|
    // +------+

    // Alternatively, a DataFrame can be created for a JSON dataset represented by
    // a Dataset<String> storing one JSON object per string.
    List<String> jsonData = Arrays.asList(
            "{\"name\":\"Yin\",\"address\":{\"city\":\"Columbus\",\"state\":\"Ohio\"}}");
    Dataset<String> anotherPeopleDataset = spark.createDataset(jsonData, Encoders.STRING());
    Dataset<Row> anotherPeople = spark.read().json(anotherPeopleDataset);
    anotherPeople.show();
    // +---------------+----+
    // |        address|name|
    // +---------------+----+
    // |[Columbus,Ohio]| Yin|
    // +---------------+----+
    // $example off:json_dataset$
  }

  private static void runJdbcDatasetExample(SparkSession spark) {
    // $example on:jdbc_dataset$
    // Note: JDBC loading and saving can be achieved via either the load/save or jdbc methods
    // Loading data from a JDBC source
    Dataset<Row> jdbcDF = spark.read()
      .format("jdbc")
      .option("url", "jdbc:postgresql:dbserver")
      .option("dbtable", "schema.tablename")
      .option("user", "username")
      .option("password", "password")
      .load();

    Properties connectionProperties = new Properties();
    connectionProperties.put("user", "username");
    connectionProperties.put("password", "password");
    Dataset<Row> jdbcDF2 = spark.read()
      .jdbc("jdbc:postgresql:dbserver", "schema.tablename", connectionProperties);

    // Saving data to a JDBC source
    jdbcDF.write()
      .format("jdbc")
      .option("url", "jdbc:postgresql:dbserver")
      .option("dbtable", "schema.tablename")
      .option("user", "username")
      .option("password", "password")
      .save();

    jdbcDF2.write()
      .jdbc("jdbc:postgresql:dbserver", "schema.tablename", connectionProperties);

    // Specifying create table column data types on write
    jdbcDF.write()
      .option("createTableColumnTypes", "name CHAR(64), comments VARCHAR(1024)")
      .jdbc("jdbc:postgresql:dbserver", "schema.tablename", connectionProperties);
    // $example off:jdbc_dataset$
  }
}
